Dynamic speed limit for vehicles and autonomous vehicles

Patent metadata

Field	Value
Publication number	US-11823568-B2
Application number	US-201916402117-A
Country	US
Kind code	B2
Filing date	May 2, 2019
Priority date	May 2, 2019
Publication date	Nov 21, 2023
Grant date	Nov 21, 2023

Abstract

Systems and methods are provided for operating a vehicle using dynamic speed limits. A vehicle can monitor current road conditions by capturing real-time data that is indicative of a driving environment associated with a roadway being traversed by the vehicle. Using the captured real-time data, the vehicle can predict a dynamic speed limit, wherein the dynamic speed limit is a driving speed for the vehicle that is adapted for the monitored current road conditions. Additionally, the vehicle can automatically perform a driving operation for the vehicle in accordance with the dynamic speed limit, wherein the driving operation causes the vehicle to move at a driving speed that is approximately equal to the predicted dynamic speed limit.

First claim

What is claimed is:

1. A method comprising:
monitoring, by a vehicle, current road conditions using captured real-time data indicative of a driving environment associated with a roadway being traversed by the vehicle, wherein monitoring the current road conditions comprises:
capturing the real-time data from one or more vehicle sensors of the vehicle, wherein the captured real-time data is from one or more vehicle sensors sensing the current road conditions associated with the roadway being traversed by the vehicle; and
collecting federated real-time data from multiple communication points communicatively connected to the vehicle and a plurality of dynamic speed limits predicted by other vehicles, the federated real-time data indicating the driving environment associated with the roadway being traversed by the vehicle, and wherein collecting the federated real-time data and the plurality of dynamic speed limits predicted by other vehicles comprises receiving data via vehicle-to-vehicle (V2V) communication between the vehicle and one or more other vehicles on the roadway;

predicting, by the vehicle, a dynamic speed limit based on the captured real-time data from the one or more vehicle sensors of the vehicle, wherein the predicted dynamic speed limit comprises a driving speed for the vehicle that is adapted for the monitored current road conditions;
optimizing the predicted dynamic speed limit by applying the federated real-time data and the plurality of dynamic speed limits predicted by other vehicles to analysis of the dynamic speed limit to generate an optimized dynamic speed limit when the predicted dynamic speed limit is not verified; and
automatically performing a driving operation for the vehicle in accordance with the optimized dynamic speed limit, wherein the driving operation causes the vehicle to move at the driving speed that is approximately equal to the optimized dynamic speed limit when the predicted dynamic speed limit is not verified.

2. The method of claim 1 , wherein predicting the dynamic speed limit comprises applying the captured real-time data to one or more machine learning models.

3. The method of claim 2 , further comprising:
determining whether the predicted dynamic speed limit is verified based on the federated real-time data, wherein determining that the predicted dynamic speed limit is verified comprises identifying a convergence between the federated real-time data and the captured real-time data and determining that the predicted dynamic speed limit is not verified comprises identifying a divergence between the federated real-time data and the captured real-time data;
upon determining that the predicted dynamic speed limit is not verified, generating the optimized dynamic speed limit, wherein the optimized dynamic speed limit is based on the predicted dynamic speed limit and optimally adapted to the monitored current road conditions in accordance with federated learning techniques applied to the one or more machine learning models; and
upon determining that the predicted dynamic speed limit is not verified, performing the driving operation for the vehicle in accordance with the optimized dynamic speed limit, wherein the driving operation causes the vehicle to move at the driving speed that is approximately equal to the optimized dynamic speed limit.

4. The method of claim 1 , wherein performing the driving operation for the vehicle in accordance with the predicted dynamic speed limit is fully autonomous.

5. The method of claim 1 , wherein performing the driving operation for the vehicle in accordance with the predicted dynamic speed limit is semi-autonomous.

6. The method of claim 5 , wherein performing the driving operation for the vehicle in accordance with the predicted dynamic speed limit comprises automatically displaying the predicted dynamic speed limit in a dashboard display of the vehicle.

7. The method of claim 1 , wherein the collecting federated real-time data from multiple communication points further comprises vehicle-to-infrastructure (V2I) communication between the vehicle and an infrastructure device.

8. The method of claim 3 , further comprising:
upon determining that the predicted dynamic speed limit is verified, performing the driving operation for the vehicle in accordance with the predicted dynamic speed limit, wherein the driving operation causes the vehicle to move at the driving speed that is approximately equal to the predicted dynamic speed limit.

Assignees

• Toyota Motor North America Inc

Inventors

• Lerner Emily
• Gardner Mathew D
• Denthumdas Shravanthi

Classifications

• B60K35/28

  characterised by the type of the output information, e.g. video entertainment or vehicle dynamics information; characterised by the purpose of the output information, e.g. for attracting the attention of the driver · CPC title

• B60K35/26

  using acoustic output · CPC title

• B60K35/21

  using visual output, e.g. blinking lights or matrix displays · CPC title

• G08G1/0125 Primary

  Traffic data processing · CPC title

• G05D1/0088

  characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours (using knowledge based models G06N5/00) · CPC title